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A Hierarchical Ship Detection Scheme for High-Resolution SAR Images

机译:高分辨率SAR图像的分层舰船检测方案

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This paper presents a new hierarchical scheme for detecting ships from high-resolution synthetic aperture radar (SAR) images. The scheme consists of two stages: detection and discrimination. In the detection stage, the existing internal Hermitian product is extended to obtain a new detector. The new detector makes a combined use of the complex coherence among more than two subapertures and the intensity of each subaperture. When the subaperture number is increased, the target/clutter contrast is shown to be improved. Ship candidates are obtained by applying a threshold. Ship discrimination is performed by using one-class classification. The covariance descriptor, developed by Tuzel in 2006, is introduced to SAR ship discrimination as the feature. The traditional one-class quadratic discriminator is used as the discriminator. After this stage, most false alarms are rejected, and the real ship targets in the candidates are maintained. The effectiveness of the proposed scheme is verified using RADARSAT-2 data. Experimental results show that the proposed scheme can detect most ship targets in the image and few false alarms occur.
机译:本文提出了一种新的分层方案,用于从高分辨率合成孔径雷达(SAR)图像中检测船舶。该方案包括两个阶段:侦查和歧视。在检测阶段,扩展现有的内部Hermitian产品以获得新的检测器。新的探测器结合了两个以上子孔径之间的复杂相干性以及每个子孔径的强度。当子孔径数增加时,目标/杂波对比度会提高。候选船舶是通过应用阈值获得的。通过使用一类分类来进行船舶区分。由图泽尔(Tuzel)在2006年开发的协方差描述符被引入SAR船舶识别中。传统的一类二次鉴别器用作鉴别器。在此阶段之后,将拒绝大多数错误警报,并保留候选对象中的实际船目标。使用RADARSAT-2数据验证了所提方案的有效性。实验结果表明,该方案能够检测出图像中的大多数舰船目标,几乎没有误报发生。

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